This book presents a consistent methodology for making decisions under uncertain conditions, as is almost always the case. Tools such as value of information and value of flexibility are explored as a means to make more complex and nuanced decisions.
The book develops the complete formalism for assessing the value of acquiring information with two novel approaches. Firstly, it integrates the fuzzy characteristics of data, and secondly develops a methodology for assessing data acquisition actions that optimize the value of projects from a holistic perspective. The book also discusses the formalism for including flexibility in the project decision assessment. Practical examples of oil- and gas-related decision problems are included and discussed to facilitate the learning process.
This book provides valuable advice and case studies applicable to engineers, researchers, and graduate students, particularly in the oil and gas industry and pharmaceutic industry.
Author(s): Martin J. Vilela, Gbenga F. Oluyemi
Series: Petroleum Engineering
Publisher: Springer
Year: 2021
Language: English
Pages: 301
City: Cham
Preface
Contents
List of Figures
List of Tables
1 Decision-Making: Concepts, Principles, and Uncertainty
1.1 Introduction to Decisions
1.2 Overview of Decision Analysis
1.3 Uncertainty, Risk, Vagueness
1.4 Review of Methods for Decision-Making
1.5 Summary
References
2 Utility Theory
2.1 Introduction to Utility Theory
2.2 Historical Development of the Utility Theory Concept
2.3 Risk Attitude
2.4 Positive Affine Transformation
2.5 Certainty Equivalent
2.6 Formulation of Utility Theory
2.7 Utility Theory: Decision Making Under Risk
2.8 Utility Theory: Decision Making Under Uncertainty
2.9 Bayesianism
2.10 Violation of Savage’s Theory and Prospect Theory
2.11 Summary
References
3 Probability and Inference Theory
3.1 Introduction to Probability
3.2 Operations with Probability
3.3 Population, Sample, Random Variables
3.4 Formulation of the Bayes Theorem and Its Meaning—The Value of Information
3.5 Sampling. Central Limit Theorem
3.6 Additional Probability Distributions
3.7 Statistical Inference
3.8 Summary
Reference
4 Probabilistic Evaluation of Uncertainties: Monte Carlo Method
4.1 Introduction to Monte Carlo Simulation
4.2 Simple Monte Carlo Applications
4.3 Number of Monte Carlo Simulations
4.4 Monte Carlo Method Workflow
4.5 Another Sampling Technique: Latin Hypercube Sampling
4.6 Advantages and Disadvantages of Monte Carlo Simulation
4.7 Scope of Monte Carlo Simulation Applications
4.8 Summary
References
5 Assessing the Importance of the Uncertainties: Design of Experiments
5.1 Introduction to Experimental Design
5.2 Background of Design of Experiments
5.3 Design of Experiments Methodology
5.4 One-Variable-At-a-Time Design
5.5 Randomised Complete Block Design
5.6 Design of Experiments Workflow
5.7 Full Factorial Design
5.8 Summary
References
6 Fuzzy Logic
6.1 Introduction to Fuzzy Logic
6.2 Classical Set Theory
6.3 Fuzzy Set Theory
6.4 Linguistic Variables
6.5 Membership Functions
6.6 Fuzzification Process
6.7 Defuzzification Process
6.8 Fuzzy Rules
6.9 Fuzzy Inference Systems
6.10 Fuzzy Applications
6.11 Summary
Appendix
References
7 Uncertainty, Data Acquisition and Value of Information Assessment
7.1 Introduction to the Value of Information
7.2 The Meaning of the Value of Information
7.3 The Formalism of the Value of Information
7.4 Value of Information
7.5 Value of Perfect Information
7.6 Value of Imperfect Information
7.7 Value of Fuzzy Information
7.8 Value of Information Applications in the Oil and Gas Industry
7.9 Sequential Data Acquisition
7.10 Summary
References
8 The Value of Flexibility—Real Options
8.1 Introduction to the Value of Flexibility
8.2 A Brief Explanation of Valuation
8.3 Applying Real Options to Real Problems
8.4 Real Case Applications of Real Options—Flexibility
8.5 The Engineering Approach to Real Options Valuation
8.6 An Example of Flexibility in the Oil and Gas Industry
8.7 Summary
References
9 Case Studies for the Value of Information and Flexibility in the Oil and Gas Industry
9.1 Examples of the Value of Information
9.2 Examples of the Value of Flexibility
9.3 Examples of the Value of Information with Statistical Analysis
9.4 Fuzzy Data Acquisition
References
